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Found 14 papers, 4 papers with code
Modeling flexible behaviour by the interactions between hippocampus and cortex
Here, we employ interacting sequential and context-inference modules to drive model-based learning as a means to better understand experimental neuronal activity data, lesion studies, and clinical research.
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness
While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood.
Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing
These models have proven to be invaluable tools for studying another complex system known to process human language: the brain.
Computational role of sleep in memory reorganization
Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, and rapid eye movement (REM) sleep reorganizes the representations of memories.
A Hopfield-like model with complementary encodings of memories
We obtain our results by deriving macroscopic mean-field equations that yield capacity formulas for sparse examples, dense examples, and dense concepts.
Spontaneous Emerging Preference in Two-tower Language Model
With the existence of side-effects brought about by the large size of the foundation language model such as deployment cost, availability issues, and environmental cost, there is some interest in exploring other possible directions, such as a divide-and-conquer scheme.
An economic decision-making model of anticipated surprise with dynamic expectation
When making decisions under risk, people often exhibit behaviors that classical economic theories cannot explain.
Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information
We look at this problem from a new perspective where the interpretation of task solving is synthesized by quantifying how much and what previously unused information is exploited in addition to the information used to solve previous tasks.
Dimensionality reduction to maximize prediction generalization capability
Generalization of time series prediction remains an important open issue in machine learning, wherein earlier methods have either large generalization error or local minima.
Learning poly-synaptic paths with traveling waves
Traveling waves are commonly observed across the brain.
On the achievability of blind source separation for high-dimensional nonlinear source mixtures
This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately -- when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality.
Reinforced stochastic gradient descent for deep neural network learning
Therefore, it is highly desirable to design an efficient algorithm to escape from these saddle points and reach a parameter region of better generalization capabilities.
Unsupervised feature learning from finite data by message passing: discontinuous versus continuous phase transition
This study deepens our understanding of unsupervised learning from a finite number of data, and may provide insights into its role in training deep networks.
Advanced Mean Field Theory of Restricted Boltzmann Machine
Learning in restricted Boltzmann machine is typically hard due to the computation of gradients of log-likelihood function.
